Figure 5.

[A] Comparisons of model performance for three representative machine learning approaches [linear model in blue, network-based model in green, non-linear model in red] in all 12 contrasts of interest. Performance of each model is given as c-statistic or area under the receiver operating curve [AUROC] with 95% confidence intervals. Random performance of 0.5 is depicted by a grey line, and relevant performance is shown by a dashed line at an AUROC of 0.8. Three models for assessing clinical response or clinical remission reach relevant levels of performance [blood, remission; inflamed tissue, remission; inflamed tissue, response]. See text for more details. [B] Transcripts [out of a total of 14 000] contributing most strongly to performance in the linear models assessing clinical remission and clinical response based on whole blood or inflamed tissue biopsies at Week 12. Transcripts are ranked by relative importance as described in the Methods section. Transcripts that are shared between the clinical remission and clinical response contrasts are highlighted in lighter red to indicate their robust role in driving model performance [NR2E1, NECAB1, CD177, and SLC51 for blood and OSM for tissue biopsy data].